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Abel Adekanmi Adeyi,Nathan Kura Bitrus,Luqman Chuah Abdullah,Lekan Taofeek Popoola,Maureen Chijioke-Okere,Oluwagbenga Olawale Omotara,Shihab Ezzuldin M.Saber 대한환경공학회 2023 Environmental Engineering Research Vol.28 No.1
Manganese dioxide was laden hooked on biochar sourced from chicken feather to obtain a biochar-supported manganese dioxide (BSM) composite. In order to reduce the costs of acquisition and minimise the disposal of adsorbents, prepared BSM composite were employed in the sequestration of Levofloxacin (LEVO) from aqueous environment. The physico-chemical features and the adsorption mechanisms of prepared BSM, prior and after the adsorption of LEVO molecules were revealed by Scanning Electron Microscopy and Fourier Transform Infrared spectroscopy techniques. The influence of adsorption parameters including BSM dose, initial concentration, temperature and residence time were studied. The removal of LEVO was significantly influenced by all parameters. Equilibrium data has its fitness in the following order: Redlich-Peterson ˃ Langmuir ˃ Freundlich models. The maximum adsorption capacity of BSM for LEVO was 104.13 mg/g. The kinetic analysis indicates best fittings for pseudo-second-order model suggesting chemisorption as controlled mechanism. Besides, liquid film and intra-particle diffusion had a vital influence on the LEVO sequestration process. Exothermic and spontaneous nature of LEVO uptake by BSM was revealed by thermodynamic analysis. The findings suggested that prepared BSM show high sorption capacity, and recyclability potential towards separation of LEVO from contaminated pharmaceutical wastewater.
Lau Kia Li,Siti Nurul Ain Md. Jamil,Luqman Chuah Abdullah,Nik Nor Liyana Nik Ibrahim,Adeyi Abel Adekanmi,Mohsen Nourouzi 대한환경공학회 2020 Environmental Engineering Research Vol.25 No.6
This research reports application of artificial neural network (ANN) in investigation and optimisation of boron adsorption capacity in aqueous solution using amidoxime-modified poly(acrylonitrile-co-acrylic acid) (AO-modified poly(AN-co-AA)). Both feed-forward and recurrent ANN have been utilized to predict the adsorption potential of synthesised polymer. Three operational parameters, which are adsorbent dosage, initial pH and initial boron concentration during adsorption process were designed to study their effects on the removal capacity. The ANN was trained from experimental data and serviced to optimize, develop and create various prediction models in the process of boron adsorption by AO-modified poly(AN-co-AA). Among several models, radial basis function (RBF) with orthogonal least square (OLS) algorithm displayed good prediction on boron adsorption capacity with mean square error (MSE) and coefficient of determination (R²) at 0.000209 and 0.9985, respectively. With desirable the MSE and R² values, ANN worked as a promising prediction tool that was able to generate good estimate. The simulated maximum adsorption capacity of the synthesized polymer is 15.23 ± 1.05 mg boron/g adsorbent. Besides, from the results of ANN, the AO-modified poly(AN-co-AA) was proven to be a potential adsorbent for the removal of boron in wastewater treatment.